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research:projects [2020/03/05 11:36] Alejandro Agostini |
research:projects [2020/03/05 15:01] Alejandro Agostini |
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===== Current Projects ===== | ===== Current Projects ===== | ||
- | **Seamless Levels of Abstraction for Robot Cognition** - (FWF Lise Meitner Project, 2019-2021): The project tackles the problem of lack of transferability and robustness of robotic cognitive architectures for the execution of everyday tasks. These architectures integrate artificial intelligence (AI) and robotic techniques that were conceived independently of each of other to solve problems at different levels of abstractions, ranging from finding abstract instructions, e.g. “pick the cup from the table”, to specific object trajectories. This makes integration and consistency checking across them very complicated. We propose a unified approach that permits searching for feasible solutions at all the levels of abstractions simultaneously, where symbolic descriptions are only evaluated from the physical parameters they represent. The unified approach estimates the density of physical experiences that permitted a successful (or failed) execution of tasks to quickly generate feasible solutions for new tasks using AI planning mechanisms. | + | **SEAMLESS LEVELS OF ABSTRACTION FOR ROBOT COGNITION** - (Austrian Science Fund (FWF) - Lise Meitner Project, 2019-2021): The project seeks to develop a robotic cognitive architecture that overcomes the difficulties found when integrating different levels of abstractions (e.g. AI and robotic techniques) for task plan and execution in unstructured scenarios. The backbone of the project is a unified approach that permits searching for feasible solutions for new tasks execution at all the levels of abstractions simultaneously, where symbolic descriptions are no longer disentangled from the physical aspects they represent. |
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**OLIVER** - Open-Ended Learning for Interactive Robots (EUREGIO IPN, 2019-2022): We would like to be able to teach robots to perform a great variety of tasks, including collaborative tasks, and tasks not specifically foreseen by its designers. Thus, the space of potentially-important aspects of perception and action is by necessity extremely large, since every aspect may become important at some point in time. Conventional machine learning methods cannot be directly applied in such unconstrained circumstances, as the training demands increase with the sizes of the input and output spaces. | **OLIVER** - Open-Ended Learning for Interactive Robots (EUREGIO IPN, 2019-2022): We would like to be able to teach robots to perform a great variety of tasks, including collaborative tasks, and tasks not specifically foreseen by its designers. Thus, the space of potentially-important aspects of perception and action is by necessity extremely large, since every aspect may become important at some point in time. Conventional machine learning methods cannot be directly applied in such unconstrained circumstances, as the training demands increase with the sizes of the input and output spaces. |